Contact position analysis of deep brain stimulation electrodes on post-operative CT images

Hemm, Simone; Coste, Jérôme; Gabrillargues, Jean; Ouchchane, Lemlih; Sarry, Laurent; Caire, François; Vassal, François; Nuti, C.; Derost, Philippe; Durif, Franck; Lemaire, Jean‐Jacques

doi:10.1007/s00701-009-0393-3

Cited by 54 publications

(39 citation statements)

References 23 publications

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“…Finally, a group of important limitations associated with modeling DBS are the substantial errors that can arise from image co-registration [Klein et al, 2009], imprecise localization of the DBS electrode in a post-operative CT [Hemm et al, 2009], general ambiguity of tractography [Thomas et al, 2014], and uncertainty in various parameters used to define the patient-specific volume conductor model [Miocinovic et al, 2009; Howell and McIntyre, 2016]. However, by using the same underlying imaging data and model parameters for all of our simulations, we were able to hold these general errors constant and focus on quantifying the errors that come about from using a predictive algorithm in place of a full patient-specific FC PAM.…”

Section: Discussionmentioning

confidence: 99%

Quantifying axonal responses in patient-specific models of subthalamic deep brain stimulation

2018

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Medical imaging has played a major role in defining the general anatomical targets for deep brain stimulation (DBS) therapies. However, specifics on the underlying brain circuitry that is directly modulated by DBS electric fields remain relatively undefined. Detailed biophysical modeling of DBS provides an approach to quantify the theoretical responses to stimulation at the cellular level, and has established a key role for axonal activation in the therapeutic mechanisms of DBS. Estimates of DBS-induced axonal activation can then be coupled with advances in defining the structural connectome of the human brain to provide insight into the modulated brain circuitry and possible correlations with clinical outcomes. These pathway-activation models (PAMs) represent powerful tools for DBS research, but the theoretical predictions are highly dependent upon the underlying assumptions of the particular modeling strategy used to create the PAM. In general, three types of PAMs are used to estimate activation: 1) field-cable (FC) models, 2) driving force (DF) models, and 3) volume of tissue activated (VTA) models. FC models represent the “gold standard” for analysis but at the cost of extreme technical demands and computational resources. Consequently, DF and VTA PAMs, derived from simplified FC models, are typically used in clinical research studies, but the relative accuracy of these implementations is unknown. Therefore, we performed a head-to-head comparison of the different PAMs, specifically evaluating DBS of three different axonal pathways in the subthalamic region. The DF PAM was markedly more accurate than the VTA PAMs, but none of these simplified models were able to match the results of the patient-specific FC PAM across all pathways and combinations of stimulus parameters. These results highlight the limitations of using simplified predictors to estimate axonal stimulation and emphasize the need for novel algorithms that are both biophysically realistic and computationally simple.

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Section: Discussionmentioning

confidence: 99%

Quantifying axonal responses in patient-specific models of subthalamic deep brain stimulation

2018

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“…In 1 case, the hemorrhage was within the pineal tumor and may have been responsible for the transient clinical deterioration. For the STN-DBS patients, the mean preoperative L -dopa response was 52.55% and the mean L -dopa response status was 14.25 [4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19]. The mean UPDRS III improvement at 6 months was 75% (mean ‘off med' score before surgery = 30.8 [13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45], mean score ‘on stim/on med' = 7.8 [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17], mean ‘on stim/off med' score after 6 months = 14.15 [5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26…”

Section: Resultsmentioning

confidence: 99%

“…In our experience, the registration of 2 CT scans with a high-resolution matrix is very accurate. This type of measurement is often performed to check the accuracy of the DBS lead's position after electrode implantation [17], [18]. Thus, although this measurement method presents an intrinsic error, we consider that it is minimal and that this is the most suitable method for assessing the accuracy of stereotactic surgery a posteriori.…”

Section: Discussionmentioning

confidence: 99%

The Impact of the Reference Imaging Modality, Registration Method and Intraoperative Flat-Panel Computed Tomography on the Accuracy of the ROSA® Stereotactic Robot

Lefranc

Capel²,

Pruvot³

et al. 2014

Stereotact Funct Neurosurg

102

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Objective: To establish the impact of the imaging modality, registration method and use of intraoperative computed tomography (CT) scan on the accuracy of the ROSA® stereotactic robot. Methods: Using a dedicated phantom device, we measured the accuracy of the stereotactic robot for 20 targets as a function of the registration method (frameless, FL, or frame-based, FB) and the reference imaging modality (3T magnetic resonance imaging, MRI, CT scanner or flat-panel CT, fpCT). We performed a retrospective study of the accuracy of the first 26 FB and 31 FL robotized stereotactic surgeries performed in our department. Results: In a phantom study, the mean target accuracy was 1.59 mm for 3T MRI-guided FL surgery, 0.3 mm for fpCT-guided FL surgery and 0.3 mm for CT-guided FB surgery. In our retrospective series, the mean accuracy was 0.81 mm for FB stereotactic surgery, 1.22 mm for our 24 stereotactic surgery procedures with FL (surface recognition) registration and 0.7 mm for our 7 stereotactic surgery procedures with FL fiducial marker registration. Intraoperative fpCT fully corrected all the registration errors. Conclusions: The ROSA stereotactic robot is highly accurate. Robotized FB stereotactic surgery is more accurate than robotized FL stereotactic surgery.

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“…In contrast to fluoroscopy and X-ray imaging, a postoperative CT is part of the standard imaging protocol in most DBS centers to exclude possible surgical complications such as cerebral hemorrhage or ischemia [12, 13]. Additionally, postoperative CT imaging is used to assess the position of electrodes with respect to the patient coordinate system defined by the anterior and posterior commissure and the mid-sagittal plane or by registering the CT to the preoperative MRI [14-17]. …”

Section: Introductionmentioning

confidence: 99%

DiODe: Directional Orientation Detection of Segmented Deep Brain Stimulation Leads: A Sequential Algorithm Based on CT Imaging

Hellerbach

Dembek

Hoevels

et al. 2018

Stereotact Funct Neurosurg

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Background: Directional deep brain stimulation (DBS) allows steering the stimulation in an axial direction which offers greater flexibility in programming. However, accurate anatomical visualization of the lead orientation is required for interpreting the observed stimulation effects and to guide programming. Objectives: In this study we aimed to develop and test an accurate and robust algorithm for determining the orientation of segmented electrodes based on standard postoperative CT imaging used in DBS. Methods: Orientation angles of directional leads (Cartesia^TM; Boston Scientific, Marlborough, MA, USA) were determined using CT imaging. Therefore, a sequential algorithm was developed that quantitatively compares the similarity of the observed CT artifacts with calculated artifact patterns based on the lead’s orientation marker and a geometric model of the segmented electrodes. Measurements of seven ground truth phantoms and three leads with 60 different configurations of lead implantation and orientation angles were analyzed for validation. Results: The accuracy of the determined electrode orientation angles was –0.6 ± 1.5° (range: –5.4 to 4.2°). This accuracy proved to be sufficiently high to resolve even subtle differences between individual leads. Conclusions: The presented algorithm is user independent and provides highly accurate results for the orientation of the segmented electrodes for all angular constellations that typically occur in clinical cases.

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Contact position analysis of deep brain stimulation electrodes on post-operative CT images

Cited by 54 publications

References 23 publications

Quantifying axonal responses in patient-specific models of subthalamic deep brain stimulation

Quantifying axonal responses in patient-specific models of subthalamic deep brain stimulation

The Impact of the Reference Imaging Modality, Registration Method and Intraoperative Flat-Panel Computed Tomography on the Accuracy of the ROSA® Stereotactic Robot

DiODe: Directional Orientation Detection of Segmented Deep Brain Stimulation Leads: A Sequential Algorithm Based on CT Imaging

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